{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "48917aca-15f4-45a4-a214-009bf12e29ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b2712a67-a70f-4e32-b881-dafb520fafc7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      39.1\n",
       "1      39.5\n",
       "2      40.3\n",
       "3       NaN\n",
       "4      36.7\n",
       "       ... \n",
       "147    36.6\n",
       "148    36.0\n",
       "149    37.8\n",
       "150    36.0\n",
       "151    41.5\n",
       "Name: bill_length_mm, Length: 152, dtype: float64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 导入本节示例数据\n",
    "df1 = sns.load_dataset(\"penguins\").query('species == \"Adelie\"')\n",
    "s1 = df1['bill_length_mm']\n",
    "s1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b4904f8-9d9b-47c6-aefe-4d37fe70d369",
   "metadata": {},
   "source": [
    "## 对Series进行绘图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "35473197-1771-4e21-ad9e-07af283b321e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='bill_length_mm', ylabel='Count'>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 把s1绘制为直方图\n",
    "sns.histplot(s1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "aed49cfe-9123-4a3a-b658-0e1961196d6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eLVy4sEmCAQAA1KVRRSY4ONhr3s/PTzExMVq2bJnGjRvXJMEAAADq0qgi8+qrrzZ1DgAAgAa7oXtk9u3bpzfeeENvvPGGDhw40ODXf/zxx5owYYJ69uwph8OhjRs3eq1/9NFH5XA4vKbx48ffSGQAANCKNOoTmdOnT+uRRx5Rdna2unXrJkk6e/asRo0apfXr16tHjx712k95ebkGDRqkxx9//Lr7bq4aP3681ydAAQEBjYkMAABaoUYVmdmzZ+vcuXP6/PPPFRcXJ0n629/+punTp+vpp5/WW2+9Va/9JCUlKSkpqdZtAgICFBER0ZiYAACglWvUpaUtW7boxRdf9JQYSRowYIAyMzP14YcfNlk4ScrOzlZYWJhiYmL01FNP6cyZM026fwAAYK5GfSJTVVUlf3//65b7+/urqqrqhkNdNX78eE2ePFl9+vTRsWPH9G//9m9KSkpSbm6u2rVrV+1r3G633G63Z97lcjVZHgAA4Fsa9YnM6NGjNWfOHJ06dcqz7KuvvtK8efM0ZsyYJgv3yCOP6Mc//rEGDhyoSZMmafPmzdqzZ4+ys7NrfE1GRoaCg4M9U1RUVJPlAQAAvqVRReZ//ud/5HK5FB0drVtuuUW33HKL+vTpI5fLpd/97ndNndGjb9++Cg0N1dGjR2vcJj09XWVlZZ7p5MmTzZYHAADYq1GXlqKiorR//35t375dR44ckSTFxcVp7NixTRru+7788kudOXNGkZGRNW4TEBDAbzYBANBGNOgTmY8++kgDBgyQy+WSw+HQ/fffr9mzZ2v27NkaNmyYbrvtNn3yySf13t/58+eVl5envLw8SdLx48eVl5enwsJCnT9/Xs8884x27dqlEydOKCsrSxMnTlS/fv2UmJjYoIMEAACtU4OKzKpVq/TEE08oKCjounXBwcGaNWuWVqxYUe/97d27V0OGDNGQIUMkSWlpaRoyZIgWLVqkdu3a6eDBg/rxj3+sW2+9VTNmzNDQoUP1ySef8IkLAACQ1MBLS5999pl+85vf1Lh+3LhxWr58eb33N3LkSFmWVeP6rVu3NiQeAABoYxr0iUxJSUm1v3Z9Vfv27fX111/fcCgAAID6aFCRufnmm5Wfn1/j+oMHD9Z6Iy4AAEBTalCReeCBB7Rw4UJVVlZet66iokKLFy/Wj370oyYLBwAAUJsG3SOzYMEC/fnPf9att96q1NRUxcTESJKOHDmizMxMXblyRb/+9a+bJSgAAMD3NajIhIeHa+fOnXrqqaeUnp7uuVHX4XAoMTFRmZmZCg8Pb5agAAAA39fgL8Tr3bu3/vKXv+jbb7/V0aNHZVmW+vfvr5tuuqk58gEAANSoUd/sK0k33XSThg0b1pRZAAAAGqRRz1oCAADwBRQZAABgLIoMAAAwFkUGAAAYiyIDAACMRZEBAADGosgAAABjUWQAAICxKDIAAMBYjf5mXwBA23L48GG7IzRIaGionE6n3THQzCgyAIBaVZSdkeTQ1KlT7Y7SIJ06ddaRI4cpM60cRQYAUKtLF85JsjR4ynz16BNrd5x6cRWd0O41S1VaWkqRaeUoMgCAeuka5lSIM8buGIAXbvYFAADGosgAAABjUWQAAICxKDIAAMBYFBkAAGAsigwAADAWRQYAABiLIgMAAIxFkQEAAMaiyAAAAGNRZAAAgLEoMgAAwFgUGQAAYCyKDAAAMBZFBgAAGIsiAwAAjEWRAQAAxqLIAAAAY1FkAACAsSgyAADAWBQZAABgLIoMAAAwFkUGAAAYiyIDAACMRZEBAADGosgAAABjUWQAAICxKDIAAMBYthaZjz/+WBMmTFDPnj3lcDi0ceNGr/WWZWnRokWKjIxUp06dNHbsWH3xxRf2hAUAAD7H1iJTXl6uQYMGKTMzs9r1zz//vP77v/9bL730knbv3q0uXbooMTFRlZWVLZwUAAD4ovZ2vnlSUpKSkpKqXWdZllatWqUFCxZo4sSJkqTXX39d4eHh2rhxox555JGWjAoAAHyQz94jc/z4cRUXF2vs2LGeZcHBwRo+fLhyc3NrfJ3b7ZbL5fKaAABA6+SzRaa4uFiSFB4e7rU8PDzcs646GRkZCg4O9kxRUVHNmhMAANjHZ4tMY6Wnp6usrMwznTx50u5IAACgmfhskYmIiJAklZSUeC0vKSnxrKtOQECAgoKCvCYAANA6+WyR6dOnjyIiIpSVleVZ5nK5tHv3biUkJNiYDAAA+Apbf2vp/PnzOnr0qGf++PHjysvLU0hIiJxOp+bOnat///d/V//+/dWnTx8tXLhQPXv21KRJk+wLDQAAfIatRWbv3r0aNWqUZz4tLU2SNH36dK1du1a//OUvVV5erpkzZ+rs2bO6++67tWXLFnXs2NGuyAAAwIfYWmRGjhwpy7JqXO9wOLRs2TItW7asBVMBAABT+Ow9MgAAAHWhyAAAAGNRZAAAgLEoMgAAwFgUGQAAYCyKDAAAMBZFBgAAGIsiAwAAjEWRAQAAxqLIAAAAY1FkAACAsSgyAADAWBQZAABgLIoMAAAwFkUGAAAYiyIDAACMRZEBAADGosgAAABjUWQAAICxKDIAAMBYFBkAAGAsigwAADAWRQYAABiLIgMAAIxFkQEAAMaiyAAAAGNRZAAAgLHa2x0ALa+wsFClpaV2x6i3w4cP2x0BAOCjKDJtTGFhoWJj41RRccHuKA12yX3R7ggAAB9DkWljSktLVVFxQcMfX6ygyGi749RL0aFc5W96WZcvX7Y7CgDAx1Bk2qigyGiFOGPsjlEvrqITdkcAAPgobvYFAADGosgAAABjUWQAAICxKDIAAMBYFBkAAGAsigwAADAWRQYAABiLIgMAAIxFkQEAAMaiyAAAAGNRZAAAgLEoMgAAwFgUGQAAYCyKDAAAMBZFBgAAGIsiAwAAjOXTRWbJkiVyOBxeU2xsrN2xAACAj2hvd4C63Hbbbdq+fbtnvn17n48MAABaiM+3gvbt2ysiIsLuGAAAwAf59KUlSfriiy/Us2dP9e3bV//yL/+iwsJCuyMBAAAf4dOfyAwfPlxr165VTEyMioqKtHTpUt1zzz3Kz89XYGBgta9xu91yu92eeZfL1VJxAQBAC/PpIpOUlOT5c3x8vIYPH67evXvrnXfe0YwZM6p9TUZGhpYuXdpSEQEAgI18/tLStbp166Zbb71VR48erXGb9PR0lZWVeaaTJ0+2YEIAANCSjCoy58+f17FjxxQZGVnjNgEBAQoKCvKaAABA6+TTReZf//VflZOToxMnTmjnzp36p3/6J7Vr104//elP7Y4GAAB8gE/fI/Pll1/qpz/9qc6cOaMePXro7rvv1q5du9SjRw+7owEAAB/g00Vm/fr1dkcAAAA+zKcvLQEAANSGIgMAAIxFkQEAAMaiyAAAAGNRZAAAgLEoMgAAwFgUGQAAYCyKDAAAMBZFBgAAGIsiAwAAjEWRAQAAxqLIAAAAY1FkAACAsSgyAADAWBQZAABgLIoMAAAwVnu7AwAAgO8UFhaqtLTU7hgNEhoaKqfTadv7U2QAAPABhYWFio2NU0XFBbujNEinTp115Mhh28oMRQYAAB9QWlqqiooLGv74YgVFRtsdp15cRSe0e81SlZaWUmQAAIAUFBmtEGeM3TGMwc2+AADAWBQZAABgLIoMAAAwFkUGAAAYiyIDAACMRZEBAADGosgAAABjUWQAAICx+EK8G2DiMzEOHz5sdwQAaDEmnfNMyupLKDKNZOozMa665L5odwQAaDYVZWckOTR16lS7ozQY5+eGocg0konPxJCkokO5yt/0si5fvmx3FABoNpcunJNkafCU+erRJ9buOPXC+blxKDI3yLRnYriKTtgdAQBaTNcwpzHnaM7PjcPNvgAAwFgUGQAAYCyKDAAAMBZFBgAAGIsiAwAAjEWRAQAAxqLIAAAAY1FkAACAsSgyAADAWBQZAABgLIoMAAAwFkUGAAAYiyIDAACMRZEBAADGosgAAABjUWQAAICxjCgymZmZio6OVseOHTV8+HB9+umndkcCAAA+wOeLzNtvv620tDQtXrxY+/fv16BBg5SYmKjTp0/bHQ0AANjM54vMihUr9MQTT+ixxx7TgAED9NJLL6lz585as2aN3dEAAIDNfLrIXLx4Ufv27dPYsWM9y/z8/DR27Fjl5ubamAwAAPiC9nYHqE1paamuXLmi8PBwr+Xh4eE6cuRIta9xu91yu92e+bKyMkmSy+Vq0mznz5+XJH3zfwp02V3RpPtuTq6i/yNJKvvqC/m3d9icpn7I3DLI3DLI3DLI3DJcxYWSvvs3san/nb26P8uyat/Q8mFfffWVJcnauXOn1/JnnnnGuvPOO6t9zeLFiy1JTExMTExMTK1gOnnyZK1dwac/kQkNDVW7du1UUlLitbykpEQRERHVviY9PV1paWme+aqqKn3zzTfq3r27HI4ba7gul0tRUVE6efKkgoKCbmhfpmIMGAOJMZAYA4kxaOvHLzXvGFiWpXPnzqlnz561bufTRaZDhw4aOnSosrKyNGnSJEnfFZOsrCylpqZW+5qAgAAFBAR4LevWrVuT5goKCmqzP7RXMQaMgcQYSIyBxBi09eOXmm8MgoOD69zGp4uMJKWlpWn69Om64447dOedd2rVqlUqLy/XY489Znc0AABgM58vMg8//LC+/vprLVq0SMXFxRo8eLC2bNly3Q3AAACg7fH5IiNJqampNV5KakkBAQFavHjxdZeu2hLGgDGQGAOJMZAYg7Z+/JJvjIHDsur6vSYAAADf5NNfiAcAAFAbigwAADAWRQYAABiLIlON1atXKz4+3vN78QkJCfrwww8lSd98841mz56tmJgYderUSU6nU08//bTnUQitQW3Hfy3LspSUlCSHw6GNGze2fNBmVJ8xyM3N1ejRo9WlSxcFBQXp3nvvVUWFOY+rqEtdY1BcXKyf/exnioiIUJcuXfSDH/xAf/rTn2xM3Pyee+45ORwOzZ0717OssrJSKSkp6t69u7p27ark5OTrvsSzNfn+GLSFc+K1qvsZuKo1nxOvVdMY2HVOpMhUo1evXnruuee0b98+7d27V6NHj9bEiRP1+eef69SpUzp16pSWL1+u/Px8rV27Vlu2bNGMGTPsjt1kajv+a61ateqGvy3ZV9U1Brm5uRo/frzGjRunTz/9VHv27FFqaqr8/FrPX6m6xmDatGkqKCjQpk2bdOjQIU2ePFkPPfSQDhw4YHPy5rFnzx79/ve/V3x8vNfyefPm6YMPPtC7776rnJwcnTp1SpMnT7YpZfOqbgzawjnxqpp+Bq5qzefEq2oaA1vPiU3xTKS24KabbrL++Mc/VrvunXfesTp06GBdunSphVO1nO8f/4EDB6ybb77ZKioqsiRZGzZssC9cC7l2DIYPH24tWLDA5kQt79ox6NKli/X66697rQ8JCbH+8Ic/2BGtWZ07d87q37+/tW3bNuu+++6z5syZY1mWZZ09e9by9/e33n33Xc+2hw8ftiRZubm5NqVtHjWNQXVa4zmxruNvC+fE2sbAznNi6/nvYzO5cuWK1q9fr/LyciUkJFS7TVlZmYKCgtS+vRFfy9Mg1R3/hQsXNGXKFGVmZtb4zKvW5PtjcPr0ae3evVthYWEaMWKEwsPDdd9992nHjh12R2021f0cjBgxQm+//ba++eYbVVVVaf369aqsrNTIkSPtDdsMUlJS9OCDD2rs2LFey/ft26dLly55LY+NjZXT6VRubm5Lx2xWNY1BdVrjObG2428r58SaxsDuc2Lr+SlrYocOHVJCQoIqKyvVtWtXbdiwQQMGDLhuu9LSUj377LOaOXOmDSmbT23HP2/ePI0YMUITJ060OWXzqmkMdu3aJUlasmSJli9frsGDB+v111/XmDFjlJ+fr/79+9ucvOnU9nPwzjvv6OGHH1b37t3Vvn17de7cWRs2bFC/fv1sTt201q9fr/3792vPnj3XrSsuLlaHDh2ue55beHi4iouLWyhh86ttDL6vNZ4T6zr+tnBOrG0M/vGPf0iy75xIkalBTEyM8vLyVFZWpvfee0/Tp09XTk6OV5lxuVx68MEHNWDAAC1ZssS+sM2gpuM/evSoPvroo1Z7H8S1ahqDqqoqSdKsWbM8z/waMmSIsrKytGbNGmVkZNgZu0nV9vdg4cKFOnv2rLZv367Q0FBt3LhRDz30kD755BMNHDjQ7uhN4uTJk5ozZ462bdumjh072h3HFg0Zg9Z4Tqzr+Ddt2tTqz4l1jYHt50RbLmgZaMyYMdbMmTM98y6Xy0pISLDGjBljVVRU2JisZVw9/jlz5lgOh8Nq166dZ5Jk+fn5Wffdd5/dMZvV1TH4xz/+YUmy/vd//9dr/UMPPWRNmTLFpnQt4+oYHD161JJk5efnX7d+1qxZNqVrehs2bLAkXffzfvXvwPbt2y1J1rfffuv1OqfTaa1YscKe0E2srjG4fPmyZVmt95xY1/Gnpqa2+nNiXWNw9Xxg1zmRT2TqqaqqSm63W9J3/+tITExUQECANm3a1Cb+p3b1+JcuXaqf//znXusGDhyolStXasKECTalaxlXxyA6Olo9e/ZUQUGB1/q///3vSkpKsildy7g6BhcuXJCk634joV27dp7/nbUGY8aM0aFDh7yWPfbYY4qNjdX8+fMVFRUlf39/ZWVlKTk5WZJUUFCgwsLCGu+pM01dY9CuXbtWfU6s6/hDQ0M1a9Ysr/Wt7ZxY1xj07dvX1nMiRaYa6enpSkpKktPp1Llz57Ru3TplZ2dr69atcrlcGjdunC5cuKA33nhDLpdLLpdLktSjRw+1a9fO5vQ3rrbjj4iIqPZmNqfTqT59+tiQtnnUNgYOh0PPPPOMFi9erEGDBmnw4MF67bXXdOTIEb333nt2R28ytY1BbGys+vXrp1mzZmn58uXq3r27Nm7cqG3btmnz5s12R28ygYGBuv32272WdenSRd27d/csnzFjhtLS0hQSEqKgoCDNnj1bCQkJuuuuu+yI3OTqGoPWfk6sz89Aaz8n1mcM7DwnUmSqcfr0aU2bNk1FRUUKDg5WfHy8tm7dqvvvv1/Z2dnavXu3JF13U+Px48cVHR1tQ+KmVdvxtxV1jcHcuXNVWVmpefPm6ZtvvtGgQYO0bds23XLLLTYnbzp1jcFf/vIX/epXv9KECRN0/vx59evXT6+99poeeOABm5O3rJUrV8rPz0/Jyclyu91KTEzUiy++aHesFrN///5Wf05E3ew8J/L0awAAYCy+RwYAABiLIgMAAIxFkQEAAMaiyAAAAGNRZAAAgLEoMgAAwFgUGQAAYCyKDAAAMBZFBmhjRo4cqblz59a4Pjo6WqtWrfLMOxwObdy4UZJ04sQJORwO5eXl1fk+2dnZcjgcOnv27A3lbSp1HTcAM/GIAgBe9uzZoy5dutgdo9Gys7M1atQoffvtt+rWrZvdcQA0M4oMAC89evSwOwIA1BuXloA26PLly0pNTVVwcLBCQ0O1cOFCXX3s2vcvLTWlHTt26J577lGnTp0UFRWlp59+WuXl5Z710dHR+s///E89/vjjCgwMlNPp1Msvv+y1j507d2rw4MHq2LGj7rjjDm3cuNFzuevEiRMaNWqUJOmmm26Sw+HQo48+6nltVVWVfvnLXyokJEQRERFasmRJvbM7HA79/ve/149+9CN17txZcXFxys3N1dGjRzVy5Eh16dJFI0aM0LFjxzyvWbJkiQYPHqw1a9bI6XSqa9eu+sUvfqErV67o+eefV0REhMLCwvQf//EfjRtQABQZoC167bXX1L59e3366ad64YUXtGLFCv3xj39s1vc8duyYxo8fr+TkZB08eFBvv/22duzYodTUVK/tfvvb3+qOO+7QgQMH9Itf/EJPPfWUCgoKJEkul0sTJkzQwIEDtX//fj377LOaP3++57VRUVH605/+JEkqKChQUVGRXnjhBa/j7tKli3bv3q3nn39ey5Yt07Zt2+p9DM8++6ymTZumvLw8xcbGasqUKZo1a5bS09O1d+9eWZZ13fEcO3ZMH374obZs2aK33npLr7zyih588EF9+eWXysnJ0W9+8xstWLDA8wRpAA1kAWhT7rvvPisuLs6qqqryLJs/f74VFxdnWZZl9e7d21q5cqVnnSRrw4YNlmVZ1vHjxy1J1oEDB+p8n7/+9a+WJOvbb7+1LMuyZsyYYc2cOdNrm08++cTy8/OzKioqPO89depUz/qqqiorLCzMWr16tWVZlrV69Wqre/funu0ty7L+8Ic/eGX6/vtee9x3332317Jhw4ZZ8+fPr/NYLOu7cViwYIFnPjc315JkvfLKK55lb731ltWxY0fP/OLFi63OnTtbLpfLsywxMdGKjo62rly54lkWExNjZWRk1CsHAG98IgO0QXfddZccDodnPiEhQV988YWuXLnSbO/52Wefae3ateratatnSkxMVFVVlY4fP+7ZLj4+3vNnh8OhiIgInT59WtJ3n7LEx8erY8eOnm3uvPPOeme4dt+SFBkZ6dl3Q18fHh4uSRo4cKDXssrKSrlcLs+y6OhoBQYGem0zYMAA+fn5eS1rSA4A/x83+wJoEefPn9esWbP09NNPX7fO6XR6/uzv7++1zuFwqKqqqkky3Oi+r3391SJY3bJr91ndezbnMQJtDUUGaIO+fz/Grl271L9/f7Vr167Z3vMHP/iB/va3v6lfv36N3kdMTIzeeOMNud1uBQQESPru18Wv1aFDB0lq1k+XAPgOLi0BbVBhYaHS0tJUUFCgt956S7/73e80Z86cZn3P+fPna+fOnUpNTVVeXp6++OILvf/++9fdHFubKVOmqKqqSjNnztThw4e1detWLV++XNL//zSkd+/ecjgc2rx5s77++mudP3++WY4HgG+gyABt0LRp01RRUaE777xTKSkpmjNnjmbOnNms7xkfH6+cnBz9/e9/1z333KMhQ4Zo0aJF6tmzZ733ERQUpA8++EB5eXkaPHiwfv3rX2vRokWS5Llv5uabb9bSpUv1q1/9SuHh4Q0qSgDM47Cs//flEQBgoDfffFOPPfaYysrK1KlTJ7vjAGhh3CMDwCivv/66+vbtq5tvvlmfffaZ5s+fr4ceeogSA7RRXFoC0ChPPvmk169SXzs9+eSTzfa+xcXFmjp1quLi4jRv3jz98z//83Xf/ttQb775Zo3HcttttzVRcgDNgUtLABrl9OnTXt+Xcq2goCCFhYW1cKLGO3funEpKSqpd5+/vr969e7dwIgD1RZEBAADG4tISAAAwFkUGAAAYiyIDAACMRZEBAADGosgAAABjUWQAAICxKDIAAMBYFBkAAGCs/wtdALMO5A4LMQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 把s1绘制为密度图\n",
    "\n",
    "sns.histplot(s1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f6ad3c18-5a7a-4377-baf7-04b1e2c8624c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 把s1绘制为箱型图\n",
    "\n",
    "sns.kdeplot(s1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8368c5dc-bfcd-4716-90f8-fd73d0116cc5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 把s1绘制为小提琴图\n",
    "\n",
    "sns.boxplot(s1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff4d7d9e-9ec5-4e85-b72e-6b7fc1d2867f",
   "metadata": {},
   "source": [
    "## 对DataFrame进行绘图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4adc53b4-21b5-4136-ba94-7a937aeb10af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>species</th>\n",
       "      <th>island</th>\n",
       "      <th>bill_length_mm</th>\n",
       "      <th>bill_depth_mm</th>\n",
       "      <th>flipper_length_mm</th>\n",
       "      <th>body_mass_g</th>\n",
       "      <th>sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>39.1</td>\n",
       "      <td>18.7</td>\n",
       "      <td>181.0</td>\n",
       "      <td>3750.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>39.5</td>\n",
       "      <td>17.4</td>\n",
       "      <td>186.0</td>\n",
       "      <td>3800.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>40.3</td>\n",
       "      <td>18.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>3250.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>36.7</td>\n",
       "      <td>19.3</td>\n",
       "      <td>193.0</td>\n",
       "      <td>3450.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Dream</td>\n",
       "      <td>36.6</td>\n",
       "      <td>18.4</td>\n",
       "      <td>184.0</td>\n",
       "      <td>3475.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Dream</td>\n",
       "      <td>36.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>195.0</td>\n",
       "      <td>3450.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Dream</td>\n",
       "      <td>37.8</td>\n",
       "      <td>18.1</td>\n",
       "      <td>193.0</td>\n",
       "      <td>3750.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Dream</td>\n",
       "      <td>36.0</td>\n",
       "      <td>17.1</td>\n",
       "      <td>187.0</td>\n",
       "      <td>3700.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Dream</td>\n",
       "      <td>41.5</td>\n",
       "      <td>18.5</td>\n",
       "      <td>201.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>152 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    species     island  bill_length_mm  bill_depth_mm  flipper_length_mm  \\\n",
       "0    Adelie  Torgersen            39.1           18.7              181.0   \n",
       "1    Adelie  Torgersen            39.5           17.4              186.0   \n",
       "2    Adelie  Torgersen            40.3           18.0              195.0   \n",
       "3    Adelie  Torgersen             NaN            NaN                NaN   \n",
       "4    Adelie  Torgersen            36.7           19.3              193.0   \n",
       "..      ...        ...             ...            ...                ...   \n",
       "147  Adelie      Dream            36.6           18.4              184.0   \n",
       "148  Adelie      Dream            36.0           17.8              195.0   \n",
       "149  Adelie      Dream            37.8           18.1              193.0   \n",
       "150  Adelie      Dream            36.0           17.1              187.0   \n",
       "151  Adelie      Dream            41.5           18.5              201.0   \n",
       "\n",
       "     body_mass_g     sex  \n",
       "0         3750.0    Male  \n",
       "1         3800.0  Female  \n",
       "2         3250.0  Female  \n",
       "3            NaN     NaN  \n",
       "4         3450.0  Female  \n",
       "..           ...     ...  \n",
       "147       3475.0  Female  \n",
       "148       3450.0  Female  \n",
       "149       3750.0    Male  \n",
       "150       3700.0  Female  \n",
       "151       4000.0    Male  \n",
       "\n",
       "[152 rows x 7 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f80574f0-0af7-4451-8968-afd0d1e4c7a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 把df1的\"bill_length_mm\"列绘制为直方图\n",
    "\n",
    "sns.histplot(df1, x=\"bill_length_mm\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d79fe089-d10c-4cd1-8547-2f00d7867290",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s1 = df1['bill_length_mm']\n",
    "sns.histplot(s1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "968a3971-a1ef-434c-9777-6b7cfa934eaf",
   "metadata": {},
   "source": [
    "## 给图表添加标题和轴标签"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68d2eb55-cf9c-4872-8b2f-7558cfdfd783",
   "metadata": {},
   "source": [
    "注意：Matplotlib当前默认的字体并不支持中文展示，所以我们需要把字体换成其它支持中文的字体。\n",
    "由于每台电脑上有的字体并不系统，你可以先查询当前系统所有字体，然后把字体替换成其中某个支持中文的字体。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d2545a36-dd86-4bf6-9dca-f680b326ce61",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AniMe Matrix - MB_EN\n",
      "Arial\n",
      "Bahnschrift\n",
      "Calibri\n",
      "Cambria\n",
      "Candara\n",
      "Comic Sans MS\n",
      "Consolas\n",
      "Constantia\n",
      "Corbel\n",
      "Courier New\n",
      "DejaVu Math TeX Gyre\n",
      "DejaVu Sans\n",
      "DejaVu Sans Display\n",
      "DejaVu Sans Mono\n",
      "DejaVu Serif\n",
      "DejaVu Serif Display\n",
      "DengXian\n",
      "Ebrima\n",
      "FangSong\n",
      "Franklin Gothic Medium\n",
      "Gabriola\n",
      "Gadugi\n",
      "Georgia\n",
      "HoloLens MDL2 Assets\n",
      "Impact\n",
      "Ink Free\n",
      "Javanese Text\n",
      "KaiTi\n",
      "Leelawadee UI\n",
      "Lucida Console\n",
      "Lucida Sans Unicode\n",
      "MS Gothic\n",
      "MT Extra\n",
      "MV Boli\n",
      "Malgun Gothic\n",
      "Microsoft Himalaya\n",
      "Microsoft JhengHei\n",
      "Microsoft New Tai Lue\n",
      "Microsoft PhagsPa\n",
      "Microsoft Sans Serif\n",
      "Microsoft Tai Le\n",
      "Microsoft YaHei\n",
      "Microsoft Yi Baiti\n",
      "MingLiU-ExtB\n",
      "Mongolian Baiti\n",
      "Myanmar Text\n",
      "Nirmala UI\n",
      "Palatino Linotype\n",
      "ROG Fonts\n",
      "STIXGeneral\n",
      "STIXNonUnicode\n",
      "STIXSizeFiveSym\n",
      "STIXSizeFourSym\n",
      "STIXSizeOneSym\n",
      "STIXSizeThreeSym\n",
      "STIXSizeTwoSym\n",
      "Sans Serif Collection\n",
      "Segoe Fluent Icons\n",
      "Segoe MDL2 Assets\n",
      "Segoe Print\n",
      "Segoe Script\n",
      "Segoe UI\n",
      "Segoe UI Emoji\n",
      "Segoe UI Historic\n",
      "Segoe UI Symbol\n",
      "Segoe UI Variable\n",
      "SimHei\n",
      "SimSun\n",
      "SimSun-ExtB\n",
      "Sitka\n",
      "Sylfaen\n",
      "Symbol\n",
      "Tahoma\n",
      "Times New Roman\n",
      "Trebuchet MS\n",
      "Verdana\n",
      "Webdings\n",
      "Wingdings\n",
      "Yu Gothic\n",
      "cmb10\n",
      "cmex10\n",
      "cmmi10\n",
      "cmr10\n",
      "cmss10\n",
      "cmsy10\n",
      "cmtt10\n"
     ]
    }
   ],
   "source": [
    "# 查询当前系统所有字体\n",
    "import matplotlib\n",
    "from matplotlib.font_manager import FontManager\n",
    "\n",
    "mpl_fonts = set(f.name for f in FontManager().ttflist)\n",
    "for f in sorted(mpl_fonts):\n",
    "    print(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6de52a9d-0457-46fa-a816-636c52bb05a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 替换成Heiti TC字体（你的系统上不一定有这个字体，如果没有的话需要替换成其它的）\n",
    "matplotlib.rc(\"font\",family='Calibri')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "46c1227d-ea79-49b9-a685-158c511e995b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ADMIN\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 26679 (\\N{CJK UNIFIED IDEOGRAPH-6837}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\ADMIN\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 26412 (\\N{CJK UNIFIED IDEOGRAPH-672C}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\ADMIN\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 25968 (\\N{CJK UNIFIED IDEOGRAPH-6570}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\ADMIN\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 37327 (\\N{CJK UNIFIED IDEOGRAPH-91CF}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\ADMIN\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 20225 (\\N{CJK UNIFIED IDEOGRAPH-4F01}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\ADMIN\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 40517 (\\N{CJK UNIFIED IDEOGRAPH-9E45}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\ADMIN\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 30340 (\\N{CJK UNIFIED IDEOGRAPH-7684}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\ADMIN\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 22068 (\\N{CJK UNIFIED IDEOGRAPH-5634}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\ADMIN\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 38271 (\\N{CJK UNIFIED IDEOGRAPH-957F}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\ADMIN\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 24230 (\\N{CJK UNIFIED IDEOGRAPH-5EA6}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\ADMIN\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 20998 (\\N{CJK UNIFIED IDEOGRAPH-5206}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\ADMIN\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 24067 (\\N{CJK UNIFIED IDEOGRAPH-5E03}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\ADMIN\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 65288 (\\N{FULLWIDTH LEFT PARENTHESIS}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\ADMIN\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 21333 (\\N{CJK UNIFIED IDEOGRAPH-5355}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\ADMIN\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 20301 (\\N{CJK UNIFIED IDEOGRAPH-4F4D}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\ADMIN\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 65306 (\\N{FULLWIDTH COLON}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\ADMIN\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 65289 (\\N{FULLWIDTH RIGHT PARENTHESIS}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 把df1的\"bill_length_mm\"列绘制为直方图\n",
    "# 把图表标题设置为“Adelie企鹅样本的嘴长度分布”\n",
    "# 把图表的X轴标签设置为“嘴长度（单位：mm）”\n",
    "# 把图表的Y轴标签设置为“样本数量”\n",
    "\n",
    "sns.histplot(df1, x=\"bill_length_mm\")\n",
    "plt.title(\"Adelie企鹅样本的嘴长度分布\")\n",
    "plt.xlabel(\"嘴长度（单位：mm）\")\n",
    "plt.ylabel(\"样本数量\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75248123-c8b3-4c6a-a1df-e8c7f6abd177",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "264ff5ef-dca1-4f29-8790-060b62f1302d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1fc847c5-6db0-40e7-84fc-ac3c81f429b1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4291374f-7b87-4065-84f6-8297baa4176e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
